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Background: Approximately half of patients with Crohn's disease require ileocolonic resection. Of these, 50% will subsequently have endoscopic disease recurrence within 1 year. We aimed to evaluate the efficacy and safety of vedolizumab to prevent postoperative recurrence of Crohn's disease., Methods: REPREVIO was a double-blind, randomised, placebo-controlled trial conducted at 13 academic or teaching hospitals in France, Italy, the Netherlands, and Spain. Eligible participants were adult patients aged 18 years or older with Crohn's disease who underwent ileocolonic resection and had one or more risk factors for recurrence. Patients were randomly assigned within 4 weeks of surgery (1:1 ratio) to receive intravenous vedolizumab (300 mg) or placebo at weeks 0, 8, 16, and 24. Randomisation was performed centrally with a computer-generated validated variable block model and patients were stratified according to disease behaviour (fibrostenotic vs inflammatory or perforating). Ileocolonoscopy was performed at week 26 and videorecorded. Endoscopic recurrence was centrally assessed with the modified Rutgeerts score, a categorial score ranging from i0 to i4. The primary endpoint was the distribution of modified Rutgeerts scores between treatment groups at week 26, analysed by non-parametric methods. The first-ranked secondary endpoint was the proportion of patients with severe endoscopic recurrence of Crohn's disease at week 26 (modified Rutgeerts score ≥i2b). Primary and safety analyses included all patients who underwent randomisation and received at least one dose of study drug. The trial is registered with the EU Clinical Trial Register (EudraCT; 2015-000555-24)., Findings: Between May 16, 2017, and April 8, 2022, 84 patients were randomly assigned to treatment, of whom four did not receive study treatment, leaving 43 patients in the vedolizumab group and 37 in the placebo group. At week 26, the probability of a lower modified Rutgeerts score with vedolizumab versus placebo was 77·8% (95% CI 66·4 to 86·3; p<0·0001). Severe endoscopic recurrence was observed in ten (23·3%) of 43 patients in the vedolizumab group versus 23 (62·2%) of 37 patients in the placebo group (difference -38·9% [95% CI -56·0 to -17·3]; p=0·0004). Serious adverse events occurred in three (7·0%) of 43 patients who received vedolizumab (bilateral tubo-ovarian abscesses, thrombosed haemorrhoids, and pancreatic adenocarcinoma) and in two (5·4%) of 37 patients who received placebo (intestinal perforation related to Crohn's disease and severe abdominal pain)., Interpretation: Vedolizumab treatment within 4 weeks of ileocolonic resection was more likely to prevent endoscopic Crohn's disease recurrence than placebo, making this an attractive option for postoperative management in patients with risk factors for recurrence. Larger studies with longer follow-up would be desirable., Funding: Takeda Nederland., Competing Interests: Declaration of interests GD'H served as a consultant for AbbVie, Alimentiv, AstraZeneca, Bristol Myers Squibb, Celltrion, Eli Lilly, Exeliom Biosciences, Johnson & Johnson, Pfizer, and Takeda; and has received speakers bureau fees from AbbVie, Eli Lilly, Pfizer, Bristol Myers Squibb, and Takeda. CT declares counselling, advisory boards, transports, or fees from AbbVie, MSD, Pfizer, Takeda, Janssen, Galapagos, Lilly, Chiesi, Ferring, Kern Pharma, Fresenius Kabi, Sandoz, and Tillotts. PN has served as speaker, consultant, and advisory board member for, or has received research funding from, MSD, AbbVie, Janssen, Takeda, Sandoz, Biogen, Ferring, Adacyte, Faes, Kern, Pfizer, Vifor, Chiesi, and Tillotts. SD reports consultancy fees from AbbVie, Alimentiv, Allergan, Amgen, Applied Molecular Transport, AstraZeneca, Athos Therapeutics, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Celltrion, Dr Falk Pharma, Eli Lilly, Enthera, Ferring Pharmaceuticals, Gilead, Hospira, Inotrem, Janssen, Johnson & Johnson, Morphic, MSD, Mundipharma, Mylan, Pfizer, Roche, Sandoz, Sublimity Therapeutics, Takeda, Teladoc Health, TiGenix, UCB, Vial, and Vifor; and lecture fees from AbbVie, Amgen, Ferring Pharmaceuticals, Gilead, Janssen, Mylan, Pfizer, and Takeda. AA received consulting fees from AbbVie, Alfa Sigma, Amgen, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Celltrion, Eli-Lilly, Ferring, Galapagos, Gilead, Giuliani, Janssen, Lionhealth, Merck, Nestlé, Pfizer, Protagonist Therapeutics, Roche, Samsung Bioepis, Sandoz, Takeda, and Tillots Pharma; speaker's fees from AbbVie, AG Pharma, Amgen, Biogen, Bristol-Myers Squibb, Celltrion, Eli-Lilly, Ferring, Galapagos, Gilead, Janssen, Lionhealth, Merck, Novartis, Pfizer, Samsung Bioepis, Sandoz, Takeda, and Teva Pharmaceuticals. XR received personal fees from Galpagos, AbbVie, Janssen, Ferring, Celltrion, Takeda, Pfizer, Amgen, Lilly, and Theradiag. LP-B received personal fees from Galapagos, AbbVie, Janssen, Genentech, Alimentiv, Ferring, Tillots, Celltrion, Takeda, Pfizer, Index Pharmaceuticals, Sandoz, Celgene, Biogen, Samsung Bioepis, Inotrem, Allergan, MSD, Roche, Arena, Gilead, Amgen, Bristol Myers Squibb, Vifor, Norgine, Mylan, Lilly, Fresenius Kabi, OSE Immunotherapeutics, Enthera, Theravance, Pandion Therapeutics, Gossamer Bio, Viatris, Thermo Fisher, ONO Pharma, Mopac, Cytoki Pharma, Morphic, Prometheus, and Applied Molecular Transport. RW has participated in advisory boards or as a speaker for Janssen, AbbVie, Ferring, and Pfizer. WGNM received speaker fees from Janssen and advisory committee AbbVie, Ferring, and Takeda. MD received speaking fees from Bristol Myers Squibb, Takeda, and Galapagos; served on an advisory board for AbbVie, Bristol Myers Squibb, Celltrion, Galapagos, Janssen, and Takeda; and received grant or research support from Pfizer, Bristol Myers Squibb, Galapagos, and Janssen. KBG has received grants from Pfizer, Celltrion, and Galapagos; consultancy fees from AbbVie, Arena Pharmaceuticals, Galapagos, Gilead, Immunic Therapeutics, Janssen Pharmaceuticals, Novartis, Pfizer, Samsung Bioepsis, and Takeda; and speaker honoraria from Celltrion, Ferring, Janssen Pharmaceuticals, Novartis, Pfizer, Samsung Bioepis, Takeda, and Tillotts. BGF has received consulting fees from AbbVie, AgomAB Therapeutics, Allianthera, Amgen, AnaptysBio, Applied Molecular Transport, Arena Pharma, Azora Therapeutics, BioJamp, Biora Therapeutics, Boehringer Ingelheim, Boston Pharma, Boxer, Celgene/Bristol Myers Squibb, Connect BioPharma, Cytoki, Disc Medicine, Duality, EcoR1, Everest Clinical Research, Lilly, Equillium, Ermium, Ferring, First Wave, Galapagos, Galen Atlantica, Genentech/Roche, Gilead, Glenmark, Gossamer Pharma, GlaxoSmithKline, Hoffmann-LaRoche, Hot Spot Therapeutics, Index Pharma, Imhotex, ImmunExt, Immunic Therapeutics, Intact Therapeutics, JAKAcademy, Janssen, Japan Tobacco, Kaleido Biosciences, Landos Biopharma, Leadiant, LifeSci Capital, Lument, Merck, Millennium, MiroBio, Morphic Therapeutics, Mylan, OM Pharma, Origo BioPharma, Orphagen, Otsuka, Pandion Therapeutics, Pfizer, Prometheus Therapeutics and Diagnostics, Play to Know, Progenity, Protagonist, PTM Therapeutics, Q32 Bio, Rebiotix, RedHill, Biopharma, REDX, Roche, Sandoz, Sanofi, Seres Therapeutics, Silverback Therapeutics, Surrozen, Takeda, Teva, Thelium, Theravance, Tigenix, Tillotts, UCB Pharma, VHSquared, Viatris, and Zealand Pharma; speakers' fees from AbbVie, Takeda, Janssen, Pfizer, and Eli Lilly; support for attending meetings or travel, or both, from Takeda, AbbVie, Eli Lilly, Pfizer, Janssen, Bristol Myers Squibb, and Sanofi; and stock or stock options from Connect Biopharm and EnGene. GZ received consulting fees from Alimentiv. AM received speakers' fees from Takeda. YB has served as a consultant for AbbVie, Boehringer Ingelheim, Celltrion, Ferring, Fresenius Kabi, Galapagos, Gilead, Hospira, Iterative Health, Janssen, Lilly, Mayoli Spindler, Merck, MSD, Norgine, Pfizer, Roche, Sandoz, Sanofi, and Takeda; has received payment for lectures from AbbVie, Celltrion, Fresenius Kabi, Galapagos, Gilead, Janssen, Lilly, MSD, Pfizer, and Takeda; and reports grant support from AbbVie, Amgen, Fresenius Kabi, Janssen, Takeda, and Viatris. DL declares counselling, advisory boards, transports, or fees from AbbVie, Amgen, Celltrion, Ferring, Galapagos, Janssen, Lilly, Pfizer, Roche, Takeda, and Theradiag. AL-S declares no competing interests in the past 2 years. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

Background & Aims: Despite the success of biological therapies in treating inflammatory bowel disease, managing patients remains challenging due to the absence of reliable predictors of therapy response., Methods: In this study, we prospectively sampled 2 cohorts of patients with inflammatory bowel disease receiving the anti-integrin α4β7 antibody vedolizumab. Samples were subjected to mass cytometry; single-cell RNA sequencing; single-cell variable, diversity, and joining sequencing; serum proteomics; and multidimensional flow cytometry to comprehensively assess vedolizumab-induced immunologic changes in the peripheral blood and their potential associations with treatment response., Results: Vedolizumab treatment led to substantial alterations in the abundance of circulating immune cell lineages and modified the T-cell receptor diversity of gut-homing CD4 + memory T cells. Through integration of multimodal parameters and machine learning, we identified a significant increase in proliferating CD4 + memory T cells among nonresponders before treatment compared with responders. This predictive T-cell signature demonstrated an activated T-helper 1/T-helper 17 cell phenotype and exhibited elevated levels of integrin α4β1, potentially making these cells less susceptible to direct targeting by vedolizumab., Conclusions: These findings provide a reliable predictive classifier with significant implications for personalized inflammatory bowel disease management., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

In drug development, assessing the toxicity of candidate compounds is crucial for successfully transitioning from preclinical research to early-stage clinical trials. Drug safety is typically assessed using animal models with a manual histopathological examination of tissue sections to characterize the dose-response relationship of the compound - a time-intensive process prone to inter-observer variability and predominantly involving tedious review of cases without abnormalities. Artificial intelligence (AI) methods in pathology hold promise to accelerate this assessment and enhance reproducibility and objectivity. Here, we introduce TRACE, a model designed for toxicologic liver histopathology assessment capable of tackling a range of diagnostic tasks across multiple scales, including situations where labeled data is limited. TRACE was trained on 15 million histopathology images extracted from 46,734 digitized tissue sections from 157 preclinical studies conducted on Rattus norvegicus . We show that TRACE can perform various downstream toxicology tasks spanning histopathological response assessment, lesion severity scoring, morphological retrieval, and automatic dose-response characterization. In an independent reader study, TRACE was evaluated alongside ten board-certified veterinary pathologists and achieved higher concordance with the consensus opinion than the average of the pathologists. Our study represents a substantial leap over existing computational models in toxicology by offering the first framework for accelerating and automating toxicological pathology assessment, promoting significant progress with faster, more consistent, and reliable diagnostic processes.

Early identification of drug toxicity is essential yet challenging in drug development. At the preclinical stage, toxicity is assessed with histopathological examination of tissue sections from animal models to detect morphological lesions. To complement this analysis, toxicogenomics is increasingly employed to understand the mechanism of action of the compound and ultimately identify lesion-specific safety biomarkers for which in vitro assays can be designed. However, existing works that aim to identify morphological correlates of expression changes rely on qualitative or semi-quantitative morphological characterization and remain limited in scale or morphological diversity. Artificial intelligence (AI) offers a promising approach for quantitatively modeling this relationship at an unprecedented scale. Here, we introduce GEESE, an AI model designed to impute morphomolecular signatures in toxicology data. Our model was trained to predict 1,536 gene targets on a cohort of 8,231 hematoxylin and eosin-stained liver sections from Rattus norvegicus across 127 preclinical toxicity studies. The model, evaluated on 2,002 tissue sections from 29 held-out studies, can yield pseudo-spatially resolved gene expression maps, which we correlate with six key drug-induced liver injuries (DILI). From the resulting 25 million lesion-expression pairs, we established quantitative relations between up and downregulated genes and lesions. Validation of these signatures against toxicogenomic databases, pathway enrichment analyses, and human hepatocyte cell lines asserted their relevance. Overall, our study introduces new methods for characterizing toxicity at an unprecedented scale and granularity, paving the way for AI-driven discovery of toxicity biomarkers.

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Coeliac disease (CD) has an estimated prevalence of ∼1% in Europe with a significant gap between undiagnosed and diagnosed CD. Active case finding may help to bridge this gap yet the diagnostic yield of such active case finding in general practice by serological testing is unknown., Objective: The aim of this study was to determine (1) the frequency of diagnosed CD in the general population, and (2) to investigate the yield of active case finding by general practitioners., Methods: Electronic medical records of 207.200 patients registered in 49 general practices in The Netherlands in 2016 were analysed. An extensive search strategy, based on International Classification of Primary Care codes, free text and diagnostic test codes was performed to search CD- or gluten-related contacts., Results: The incidence of CD diagnosis in general practice in 2016 was 0.01%. The prevalence of diagnosed CD reported in the general practice in the Netherlands was 0.19%, and considerably higher than previously reported in the general population. During the one year course of the study 0.95% of the population had a gluten-related contact with their GP; most of them (72%) were prompted by gastrointestinal complaints. Serological testing was performed in 66% ( n  = 1296) of these patients and positive in only 1.6% ( n  = 21)., Conclusion: The number of diagnosed CD patients in the Netherlands is substantially higher than previously reported. This suggests that the gap between diagnosed and undiagnosed patients is lower than generally assumed. This may explain that despite a high frequency of gluten-related consultations in general practice the diagnostic yield of case finding by serological testing is low.Key pointsThe diagnostic approach of GPs regarding CD and the diagnostic yield is largely unknownCase finding in a primary health care practice has a low yield of 1.6%CD testing was mostly prompted by consultation for gastrointestinal symptomsThere is a heterogeneity in types of serological test performed in primary care.

Polyunsaturated fatty acids (PUFAs), especially the n-3 series, are known for their protective effects. Considering that cardiovascular diseases are risk factors for dementia, which is common at aging, the aim of this study was to evaluate whether fatty acid status in the elderly was associated with cognitive function and cardiovascular risk. Forty-five elderly persons (age ≥ 60 years) were included and divided into two groups based on their Mini-Mental Status Examination score adjusted for educational level: the case group (n = 12) and the control group (n = 33). Serum fatty acid composition, homocysteine (Hcy), hs-CRP, lipid profile and different cognitive domains were evaluated. The case group, characterized by reduced cognitive performance, showed higher levels of 14:0, 16:0, 16:1n-7 fatty acids and lower levels of 22:0, 24:1n-9, 22:6n-3 (DHA) and total PUFAs compared to the control group (p < 0.05). The n-6/n-3 ratio was elevated in both study groups, whereas alterations in Hcy, hs-CRP and lipid profile were observed in the case group. Cognitive function was positively associated with the 24:1n-9, DHA and total n-3 PUFAs, while 14:0, 16:0 and 16:1n-7 fatty acids, the n-6/n-3 ratio and Hcy were inversely associated. In addition, n-3 PUFAs, particularly DHA, were inversely associated with cardiovascular risk, assessed by Hcy levels in the elderly.

This study examined how operant behavior adapted to an abrupt but regular change in the timing of reinforcement. Pigeons were trained on a fixed interval (FI) 15-s schedule of reinforcement during half of each experimental session, and on an FI 45-s (Experiment 1), FI 60-s (Experiment 2), or extinction schedule (Experiment 3) during the other half. FI performance was well characterized by a mixture of two gamma-shaped distributions of responses. When a longer FI schedule was in effect in the first half of the session (Experiment 1), a constant interference by the shorter FI was observed. When a shorter FI schedule was in effect in the first half of the session (Experiments 1, 2, and 3), the transition between schedules involved a decline in responding and a progressive rightward shift in the mode of the response distribution initially centered around the short FI. These findings are discussed in terms of the constraints they impose to quantitative models of timing, and in relation to the implications for information-based models of associative learning. This article is part of a Special Issue entitled: Associative and Temporal Learning., (Copyright © 2013 Elsevier B.V. All rights reserved.)